A relational database system is a collection of data items organized as a set of formally described tables from which data can be accessed. These relational databases can become enormous, and the response to any query of these databases may require accessing a multitude of tables, each of which may be partially responsive to the query.
Many relational databases, such as in social networks, grow rapidly as data changes with respect to participants and their various natures, features, qualities, and the like. Such a network may be represented by a graph, where nodes are connected by edges to other nodes, and both the nodes and edges represent associated relational data. Thus, using a social network as an example, the nodes of the graph may represent members of the social network, while the edges represent the associations between each of the various members.
Previously, the updating and searching of these graphs has been laborious, time consuming, and inordinately and exhaustively detailed, requiring the individual treatment and assessment of each of a multiplicity of nodes and edges. Further, a graph may have to be updated each time the tables are revised requiring resources and time that may have been better suited elsewhere.
In some instances, graph analytics may be accomplished using one or more computing devices to organize and graph relational data. These computing devices may include server computers, desktop computers, laptop computers, or other similar computing devices. To perform graph analytics, graph analytics systems may first organize data items into formally described tables in what is known as the relational database, wherein the data items may include transaction data, friendship data, and purchase data, among other data—including combinations thereof. Following the input of the data items, a query may be generated to determine relationships or facts about data items. To help in answering these queries, it is often useful to generate a graph view of the relational data. This graph view includes nodes and edges that are both easier to illustrate associations in the data, as well as faster to traverse and respond to inquiries. However, to answer the queries, a graph must first be generated that corresponds to the appropriate data, which takes time and resources.